Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) complicate the hospital course of approximately 12-25% of those with severe traumatic injury. ALI is the most frequent manifestation of multiple organ failure, the leading cause of death among those initially hospitalized and surviving their injury. ALI is independently associated with longer hospital stays, increased costs, and worse long-term health related quality of life in trauma patients. Studies to identify patients at high risk for ALI and studies to identify interventions to prevent or reduce the severity of ALI can significantly affect patient outcome and resource utilization. We are still unable to accurately predict who will develop ALI after trauma, and have an incomplete understanding of its associated risk factors. The few models developed to predict post-traumatic ALI are frequently limited by the application of inconsistent ALI definitions, a lack of external validation, or an incomplete account of all potential predictors. Similarly, numerous ALI risk factors have been proposed but the cohorts from which they were determined were often comprised of mixed inciting ALI risks, with comparably smaller trauma subsets. Studies attempting to examine risk factors often lack a data-rich trauma cohort, or use poor modeling strategies which do not account for multiple and potentially confounding factors. Additionally, biologic markers of injury have not been adequately studied in a specific trauma population, and may be causally related. The main objective of this project is to generate and validate a statistical model which uses clinical factors to reliably predict post-traumatic ALI. We will subsequently identify independent, and potentially causal, risks for the development of post-traumatic ALI. The roles of commonly prescribed blood products (platelets, cryoprecipitate, fresh frozen plasma) and biologically plausible markers of lung injury (surfactant protein-D and von Willebrand factor antigen) will specifically be studied. This project carries important implications to those with severe traumatic injury. An accurate predictive model will allow us to appropriately target future preventive interventions to the trauma populations most likely to respond, and thus receive the most benefit. In addition, the identification of potentially causal and modifiable factors important to the development of ALI could translate into promising new therapies or novel biomarkers to aid in the diagnosis of ALI. [unreadable] [unreadable] [unreadable] [unreadable]